The fundus examination is a diagnosis assistance material frequently utilized in ophthalmology since it is able to observe the abnormalities of the retina, optic nerve, and macula and allows the results to be confirmed by relatively simple imaging. In recent years, the fundus examination has been increasingly used because, through the fundus examination, it is able to observe not only eye diseases but also a degree of blood vessel damage caused by chronic diseases such as hypertension and diabetes by a non-invasive method.
Meanwhile, due to the recent rapid development of deep learning technology, the development of diagnostic artificial intelligence has been actively carried out in the field of medical diagnosis, especially the field of image-based diagnosis. Global companies such as Google and IBM have invested heavily in the development of artificial intelligence for analyzing a variety of medical video data, including large-scale data input through collaborations with the medical community. Some companies have succeeded in developing an artificial intelligence diagnostic tool that outputs superior diagnostic results.
However, in a case in which a plurality of values are desired to be predicted from a single test data through a deep learning trained model, there has been a problem in that accuracy of prediction is reduced and processing speed is lowered. Accordingly, there is a need for a system for learning and diagnosis that enables accurate prediction of a plurality of diagnostic characteristics at a high data processing speed.